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Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical a...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9812610/ https://www.ncbi.nlm.nih.gov/pubmed/36619816 http://dx.doi.org/10.1155/2022/9152605 |
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author | Gao, Zhihong Lou, Lihua Wang, Meihao Sun, Zhen Chen, Xiaodong Zhang, Xiang Pan, Zhifang Hao, Haibin Zhang, Yu Quan, Shichao Yin, Shaobo Lin, Cai Shen, Xian |
author_facet | Gao, Zhihong Lou, Lihua Wang, Meihao Sun, Zhen Chen, Xiaodong Zhang, Xiang Pan, Zhifang Hao, Haibin Zhang, Yu Quan, Shichao Yin, Shaobo Lin, Cai Shen, Xian |
author_sort | Gao, Zhihong |
collection | PubMed |
description | The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed. |
format | Online Article Text |
id | pubmed-9812610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-98126102023-01-05 Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process Gao, Zhihong Lou, Lihua Wang, Meihao Sun, Zhen Chen, Xiaodong Zhang, Xiang Pan, Zhifang Hao, Haibin Zhang, Yu Quan, Shichao Yin, Shaobo Lin, Cai Shen, Xian Comput Intell Neurosci Review Article The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed. Hindawi 2022-12-28 /pmc/articles/PMC9812610/ /pubmed/36619816 http://dx.doi.org/10.1155/2022/9152605 Text en Copyright © 2022 Zhihong Gao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Gao, Zhihong Lou, Lihua Wang, Meihao Sun, Zhen Chen, Xiaodong Zhang, Xiang Pan, Zhifang Hao, Haibin Zhang, Yu Quan, Shichao Yin, Shaobo Lin, Cai Shen, Xian Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process |
title | Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process |
title_full | Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process |
title_fullStr | Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process |
title_full_unstemmed | Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process |
title_short | Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process |
title_sort | application of machine learning in intelligent medical image diagnosis and construction of intelligent service process |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9812610/ https://www.ncbi.nlm.nih.gov/pubmed/36619816 http://dx.doi.org/10.1155/2022/9152605 |
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